Class acts: the tricky business of trading new asset markets

Is it any surprise that firms have ventured off in search of new pastures, trying out different asset classes, given the persistent lull in trading activity in the world's equity markets? Adam Cox reports on what they need to consider when branching out.

Damp squib (expression, British
English):1) A miniature explosive device that has become wet,
preventing it from working;2) A disappointment;3) A description of what trading volumes in the
equity markets were like in the past couple of
years.

Is it any surprise that firms have ventured off in search of new
pastures, trying out different asset classes, given the
persistent lull in trading activity in the world's equity
markets?

Since late 2008, interest rates in the United States and many
major economies have been at rock-bottom levels and have shown
scant sign of rising. But instead of sending investors flooding
into equity markets and spurring strong volumes, the near-zero
rate environment has led to subdued and somewhat orderly trading.
Some of this torpor can be attributed to a post-crisis hangover,
with general investor confidence still fragile after such an
extreme, once-in-a-generation event. But market watchers say it
is also clearly a function of the low volatility that stems from
the ultra-stable interest rate outlook that has been signalled by
central bankers.

Faced with such an environment, many trading firms have chosen to
widen their horizons and trade new markets. A study by Barclays
Capital that questioned 1,300 institutional investors found 85
percent of respondents were responsible for multiple asset
classes. That compared with about 50 percent when the survey
started in 2005.

In Automated Trader's own survey, which featured more than 650
buy side and sell side participants, more trading was expected in
every single asset class listed. In the main cash markets, growth
was expected to be most muted, with 53 percent now saying they
trade cash equities and just 55 percent expecting to in two to
three years. But in several derivatives markets - notably equity
options, fixed income derivatives and foreign exchange futures -
the share of firms expecting to trade was some 9-11 percentage
points higher. All told, the survey highlighted the appetite
among trading firms for taking on new challenges.

Still, conversations with market participants, investors and
technologists suggest that the business of wading into new asset
classes is no simple matter. From mundane, nuts-and-bolts factors
such as connectivity and due diligence, to more complicated tasks
such as reconstructing models, firms find that trading or selling
products for a new asset class can bring up a host of issues to
consider. To that end, we set out to hear from executives,
quants, academics and others just what those issues are and what
you need to think about before diving head-first into a new
market.

This year's model

At the heart of any transition into a new asset class is the task
of coming up with new models.

"Every market has its own idiosyncrasies, different exchanges,
different trading strategies, different supply and demand
factors, that make it somewhat difficult," said Scott Morris,
head of quantitative research at Ronin Capital in Chicago.
"So you have to tailor your models for every
market."

Scott Morris

"Every market has its own idiosyncrasies, different exchanges,
different trading strategies, different supply and demand
factors, that make it somewhat difficult."

Morris said coming up with new strategies and models is harder
than people often realise because they inevitably come with a set
of biases based on their previous experience.

"It takes longer than people think. And one of the reasons it
takes so long is that you really never know until you start
trading because some of the assumptions you had in the other
asset classes, you perhaps didn't realise they were constraints.
But once you start applying them to a different asset class,
those assumptions catch up with you," he said.

One example of the kind of tripwire that a trader may face is the
liquidity profile of a given market. If you're used to one kind
of liquidity, say, in blue chip equities, you may find life very
different in commodities or fixed income.

"That's a perfect example of a hidden assumption. You develop a
model that works in a high voluminous world or asset class and
then you go to one less so, you have that assumption built into
the old model and you have to then tailor your model," he said.

Even moving from a cash market to derivatives within the same
nominal asset class can bring risks.

"There's a number of examples of equity firms moving into the
equity options space a couple of years ago, where they just saw
options as another source of delta for them to put their trades
on and it just didn't work out for them. Of course the timing
perhaps wasn't great for that move as well, but that's a good
example," Morris said.

Linked in markets

The model-making challenge is not only about accounting for how
one asset class behaves differently from another on its own, but
also about how it interacts with the wider market.

"You always have to be aware of the fact that the markets are
linked," said Rick Cooper, an assistant professor of finance at
the Illinois Institute of Technology.

Rick Cooper

"You always have to be aware of the fact that the markets are
linked."

"The classic example is that many low frequency firms had much
information to add to the economy, but they traded once a month,
because every time they came into trade, they widened the spreads
and they couldn't afford the slippage. Now, with high frequency,
some of them can trade every day, because the high frequency
traders add liquidity. So that affects the information flow, it
affects the value. It also affects how long the signal is
valuable."

By the same token, he said, derivatives markets will affect how
long a stock signal can remain valuable. "So you can build your
model sort of in isolation - 'I'm building a stock model', 'I'm
building a bond model', 'I'm building a fixed-income model' - but
how well that model works will be contingent on the dynamics of
the other markets. And you have to take that into account when
you're doing your analysis of your trade. As you start to see
things slip, it isn't always just your specific market that's
making it slip," Cooper said.

Cooper is not your typical chalk-wielding professor. For a start,
he spent years in the markets as a portfolio manager and director
of analytics before deciding to go back into academia.

He also said that when he's teaching students, he doesn't worry
so much about asset classes.

"When I teach a class I try to give them a more basic approach,
which is to say: What is it that makes money? Doesn't matter
which asset class it is, you're either going to make money
because you have a better statistical forecast, you have better
information, or you have technology that's an advantage in the
trading," he said.

"In the end, we're trying to produce a product that our employers
will want. And being in Chicago, the product we think employers
will want are people who have good applied skills, that will work
in … high frequency or derivative markets," he said. "But
we don't know if they're going to get employed by the CME or a
derivatives trader or by GETCO, so they have to be kind of a
Jack-of-All-Trades. They have to know how to think about the
problem."

Raphael Markellos, who is chair in finance for the Norwich
Business School at the University of East Anglia, also said asset
class-specifics did not really enter into the discussion with his
students. The idea of a university education was to learn about
principles, approaches and concepts which could be applied to a
whole variety of problems, he said.

"The principles are the same or the tools can be the same, but
obviously different data sets or different asset classes will
have unique characteristics, and you need experience, you need to
define the data sets and the problems that they have, the noise
that's involved," he said.

In commodities, for example, electricity or energy markets,
seasonality can be such a key part of the data. Another factor
could be the frequency and scale of extreme observations, as well
as the question of whether to even include outliers in a dataset.
"So in electricity data, you'll see a lot of these extreme
observations. They're part of the normal behaviour of the data."

Markellos's own research spans an extremely wide variety of
datasets, from wine to weather to football scores. He describes
himself as "a methodology person" who views different datasets as
challenges. "If it's a mountain, we have to climb it," he says.

As such, he has one piece of advice for anyone building a model
in any asset class. "One very important lesson - which I learned
also from my teachers - is that before you start dealing with
models, you have to really, really prepare your data properly, to
clean them and make sure you've got a good dataset to work with."

He said many people ignore this step as they already have an end
result in mind and are less concerned with how they will get
there.

Nuts & bolts

So you've spent a lot of time preparing your data and you've
taken care to challenge your built-in assumptions when it comes
to your trading model. What next?

Mike Madigan, the chief technology officer at WH Trading in
Chicago, knows a thing or two about making it possible for his
firm's traders to go after new markets. WH Trading has about 75
people trading a long list of products from interest rates to
agriculture. With three data centres in Chicago, racks in
Singapore, Tokyo and London, the firm is active on the CME,
NYMEX, COMEX, Eurex, LIFFE and other venues.

"Some of the interest rate products historically have been some
of the key products for us. And with interest rates being so low
and with volatility so low - the Fed saying it's going to be this
way until 2015 - yes, we have had to start looking for other
venues to trade in," Madigan said.

First off, there are technical issues such as making sure
software is set up for new contract specifications. That can be a
question of ticks versus decimals, but generally speaking he says
those changes don't require massive software alterations.

More challenging is the broader due diligence required to go into
a new market.

"First of all, anytime we want to go to a new exchange, we have
to do some analysis on what do we think the opportunity is. The
trader has to look at the marketplace and say, 'How much volume
is there, how much volatility is there, is this even worth going
to? Can I bring an edge?' And then, they'll sit down with me and
the owners and say, 'Look, here's how much it's going to cost -
it's not free - to go to any of these exchanges.'

"It's a fairly expensive proposition, just from a one-time cost
of going there," Madigan added, noting that on top of that is the
monthly cost of connectivity, market data feeds, routing fees and
other costs.

"So there are barriers to entry, both capital wise and frankly
just pain-in-the-neck wise, apart from the costs that it's going
to take you to do it in the first place," he said.

All told, the time from the moment a trader decides it's possible
to make money in a new market to when the firm is set up and
ready to go can take six months, Madigan said.

Simon Garland

But there could be deeper issues. Adjusting software for new
specifications or setting up exchange connections and feeds may
be relatively straight-forward. But trading different, or
multiple, asset classes can affect your overall technology
infrastructure.

Simon Garland, chief strategist at Kx Systems, said the kind of
database a firm uses can become critical.

"Part of it is just simply the speed," he said. "If you've got
the speed, that means you can afford to be taking transactions
coming from one place or another which have, in database terms,
different schemas, different representations."

The differences may be slight but in a millisecond-critical world
that still needs to be addressed. "If you can have the speed to
be normalising on the fly, that suddenly means you have proper
risk management over the asset classes," he said.

Kx's database technology is asset-class neutral - in fact it
began life as a programming language which clients began writing
databases for.

Finally, however, there is the moment of truth. Morris of Ronin
said that only when you start trading a new market does it become
clear if you did enough homework. "You can back test as much as
you want but really the essence is getting in there and finding
out the trades. So it's a lot harder than most people think."